Earthquakes Explained: Unraveling a Century of Seismic Data with Python¶

Introduction¶

This Jupyter Notebook is designed to be a comprehensive guide to understanding the complex phenomena of seismic events, utilizing Python to analyze and interpret a century's worth of seismic data.

Why Earthquakes?¶

Earthquakes are one of the most powerful natural phenomena on our planet. They have shaped landscapes and influenced civilizations. Understanding earthquakes it's crucial for predicting future events, mitigating risks, and safeguarding communities.

To understand more about Earthquakes...¶

I recommend taking a look at this webpage: https://spaceplace.nasa.gov/earthquakes/en/

Getting Started¶

So, let's start. Whether you are a student, a researcher, or just someone curious about earthquakes, this notebook is designed to provide you with a comprehensive understanding of these natural phenomena, all through the lens of Python and data analysis.

Importing our data¶

Our analysis will be based on the seismic data collected over the past century.

In [ ]:
import pandas as pd
In [ ]:
df = pd.read_csv('https://raw.githubusercontent.com/hibrantapia/Earthquakes_Explained/main/Earthquakes_1900-2023.csv')
In [ ]:
df.head()
Out[ ]:
Unnamed: 0 time latitude longitude depth mag magType nst gap dmin ... updated place type horizontalError depthError magError magNst status locationSource magSource
0 0 1900-10-09T12:25:00.000Z 57.09 -153.48 NaN 7.86 mw NaN NaN NaN ... 2022-05-09T14:44:17.838Z 16 km SW of Old Harbor, Alaska earthquake NaN NaN NaN NaN reviewed ushis pt
1 1 1901-03-03T07:45:00.000Z 36.00 -120.50 NaN 6.40 ms NaN NaN NaN ... 2018-06-04T20:43:44.000Z 12 km NNW of Parkfield, California earthquake NaN NaN NaN NaN reviewed ushis ell
2 2 1901-07-26T22:20:00.000Z 40.80 -115.70 NaN 5.00 fa NaN NaN NaN ... 2018-06-04T20:43:44.000Z 6 km SE of Elko, Nevada earthquake NaN NaN NaN NaN reviewed ushis sjg
3 3 1901-12-30T22:34:00.000Z 52.00 -160.00 NaN 7.00 ms NaN NaN NaN ... 2018-06-04T20:43:44.000Z south of Alaska earthquake NaN NaN NaN NaN reviewed ushis abe
4 4 1902-01-01T05:20:30.000Z 52.38 -167.45 NaN 7.00 ms NaN NaN NaN ... 2018-06-04T20:43:44.000Z 113 km ESE of Nikolski, Alaska earthquake NaN NaN NaN NaN reviewed ushis abe

5 rows × 23 columns

The database is called "Earthquakes_1900-2023.csv".¶

\

Disclaimer: Keep in mind, we are only reviewing earthquakes that are 5 or more on the Richter scale, if we count lower, we are looking at probably millions of earthquakes in the last 100 years.

\

Here's an explanation of each column in the USGS earthquake data:

Time: The time of the earthquake, reported as the number of milliseconds since the Unix epoch (January 1, 1970, 00:00:00 UTC).
Latitude:The latitude of the earthquake's epicenter, reported in decimal degrees.
Longitude: The longitude of the earthquake's epicenter, reported in decimal degrees.
Depth: The depth of the earthquake, reported in kilometers.
Mag: The magnitude of the earthquake, reported on various magnitude scales (see magType column below).
MagType: The magnitude type used to report the earthquake magnitude (e.g. "mb", "ml", "mw").
Nst: The total number of seismic stations used to calculate the earthquake location and magnitude.
Gap: The largest azimuthal gap between azimuthally adjacent stations (in degrees).
Dmin: The distance to the nearest station in degrees.
Rms: The root-mean-square of the residuals of the earthquake's hypocenter location.
Net: The ID of the seismic network used to locate the earthquake.
Id: A unique identifier for the earthquake event.
Updated: The time when the earthquake event was most recently updated in the catalog, reported as the number of milliseconds since the Unix epoch.
Place: A human-readable description of the earthquake's location.
Type: The type of seismic event (e.g. "earthquake", "quarry blast", "explosion").
HorizontalError: The horizontal error, in kilometers, of the location reported in the latitude and longitude columns.
DepthError: The depth error, in kilometers, of the depth column.
MagError: The estimated standard error of the reported earthquake magnitude.
MagNst: The number of seismic stations used to calculate the earthquake magnitude.
Status: The status of the earthquake event in the USGS earthquake catalog (e.g. "reviewed", "automatic").
LocationSource: The ID of the agency or network that provided the earthquake location.
MagSource: The ID of the agency or network that provided the earthquake magnitude.

Data Analysis¶

1. How many rows and columns are there in the database?¶

In [ ]:
print(df.shape)

(96115, 23)

2. A few statistics about the dataset...¶

In [ ]:
df.describe()
Out[ ]:
Unnamed: 0 latitude longitude depth mag nst gap dmin rms horizontalError depthError magError magNst
count 96115.000000 96115.000000 96115.000000 95830.000000 96115.000000 25552.000000 35847.000000 15909.000000 67374.000000 14534.000000 46406.000000 29184.000000 36174.000000
mean 48057.000000 3.563711 40.691301 62.450977 5.456296 166.031739 62.424166 4.285599 0.976135 7.606509 8.450930 0.189006 51.460082
std 27746.154899 30.083694 121.858153 108.664042 0.486570 133.639446 38.834914 5.281175 0.381032 4.893094 11.237023 0.160968 75.903997
min 0.000000 -77.080000 -179.997000 -4.000000 5.000000 0.000000 6.500000 0.001376 -1.000000 0.042000 -1.000000 0.000000 0.000000
25% 24028.500000 -17.732750 -72.197000 14.400000 5.100000 67.000000 35.100000 1.222000 0.840000 5.900000 2.900000 0.063000 11.000000
50% 48057.000000 -1.035500 99.480000 33.000000 5.300000 125.000000 54.000000 2.583000 0.990000 7.300000 5.100000 0.200000 26.000000
75% 72085.500000 29.645000 142.769500 51.000000 5.700000 228.000000 79.000000 5.167000 1.100000 9.000000 10.600000 0.250000 58.000000
max 96114.000000 87.386000 180.000000 700.000000 9.500000 929.000000 360.000000 50.901000 69.320000 99.000000 1091.900000 1.840000 941.000000

3. Earthquakes per Year¶

In [ ]:
import matplotlib.pyplot as plt

df['time'] = pd.to_datetime(df['time'])
df['year'] = df['time'].dt.year

earthquake_counts = df.groupby('year').size()

mean_earthquakes = earthquake_counts.mean()
In [ ]:
plt.style.use('dark_background')
plt.figure(figsize = (15, 5))

earthquake_counts.plot(kind = 'line', color = '#ccff66', marker = 'o', label = 'Earthquakes per Year')
plt.axhline(y = mean_earthquakes, color = 'red', linestyle = '--', label = 'Mean Earthquakes per Year')
plt.xticks(range(min(earthquake_counts.index), max(earthquake_counts.index) + 1, 10))
plt.title('Earthquakes per Year (1900-2023)')
plt.xlabel('Year')
plt.ylabel('Number of Earthquakes')
plt.legend()
plt.grid(True)
plt.show()

We notice a drop on the last year, that's because the dataset does not have the full list of Earthquakes that happened on 2023.

In [ ]:
last_earthquake = df.sort_values(by = 'time', ascending = False).iloc[0]
last_earthquake_info = last_earthquake[['time']]

print("The last recorded Earthquake the dataset happened on:", last_earthquake_info)

The last recorded Earthquake the dataset happened on: time    2023-04-02 19:32:24.808000+00:00
Name: 96096, dtype: object

4. Which year had the greatest number of earthquakes and how many were there?¶

In [ ]:
max_year = earthquake_counts.idxmax()
max_count = earthquake_counts.max()

print(f"Year with the highest number of earthquakes: {max_year} with {max_count} earthquakes.")

Year with the highest number of earthquakes: 2011 with 2621 earthquakes.

5. Histogram of the Magnitude Distribution¶

In [ ]:
df['mag'].plot(kind = 'hist', title = 'Histogram of the Magnitude Distribution', color = '#00ffff')
Out[ ]:
<Axes: title={'center': 'Histogram of the Magnitude Distribution'}, ylabel='Frequency'>

6. What has been the strongest earthquake in the last 100 years?¶

In [ ]:
plt.figure(figsize = (15, 7))

mag_5_to_6 = df[(df['mag'] >= 5) & (df['mag'] < 6)]
plt.scatter(mag_5_to_6['year'], mag_5_to_6['mag'], color = 'green', label = 'Magnitude 5 to 6')

mag_6_to_7 = df[(df['mag'] >= 6) & (df['mag'] < 7)]
plt.scatter(mag_6_to_7['year'], mag_6_to_7['mag'], color = 'yellow', label = 'Magnitude 6 to 7')

mag_7_to_8 = df[(df['mag'] >= 7) & (df['mag'] <= 8)]
plt.scatter(mag_7_to_8['year'], mag_7_to_8['mag'], color = 'red', label = 'Magnitude 7 to 8')

mag_8_to_9 = df[(df['mag'] >= 8) & (df['mag'] <= 9)]
plt.scatter(mag_8_to_9['year'], mag_8_to_9['mag'], color = '#ff00ff', label = 'Magnitude 8 to 9')

mag_9_to_95 = df[(df['mag'] >= 9) & (df['mag'] <= 9.5)]
plt.scatter(mag_9_to_95['year'], mag_9_to_95['mag'], color = 'white', label = 'Magnitude 9 to 9.5')

plt.xticks(range(min(df['year']), max(df['year']) + 1, 10))
plt.title('Magnitude of Earthquakes Per Year (1900-2023)')
plt.xlabel('Year')
plt.ylabel('Magnitude')
plt.legend()
plt.grid(True)
plt.show()
In [ ]:
magnitude_categories = {
    '5': (5, 5.99),
    '6': (6, 6.99),
    '7': (7, 7.99),
    '8': (8, 8.99),
    '9+': (9, 9.5),
}

earthquake_counts = {}
for category, (min_mag, max_mag) in magnitude_categories.items():
    count = df[(df['mag'] >= min_mag) & (df['mag'] <= max_mag)].shape[0]
    earthquake_counts[category] = count

formatted_output = "\n".join([f"Magnitude {category}: {count:,} earthquakes" for category, count in earthquake_counts.items()])
print(formatted_output)

Magnitude 5: 82,682 earthquakes
Magnitude 6: 11,997 earthquakes
Magnitude 7: 1,341 earthquakes
Magnitude 8: 90 earthquakes
Magnitude 9+: 5 earthquakes
In [ ]:
stronger_earthquake = df.loc[df['mag'].idxmax()]
print("Strongest Earthquake Information:")
print(stronger_earthquake)

Strongest Earthquake Information:
Unnamed: 0                                                      9869
time                                       1960-05-22 19:11:20+00:00
latitude                                                     -38.143
longitude                                                    -73.407
depth                                                           25.0
mag                                                              9.5
magType                                                           mw
nst                                                              NaN
gap                                                              NaN
dmin                                                             NaN
rms                                                              NaN
net                                                         official
id                                         official19600522191120_30
updated                                     2022-04-26T16:30:06.879Z
place              1960 Great Chilean Earthquake (Valdivia Earthq...
type                                                      earthquake
horizontalError                                                  NaN
depthError                                                       NaN
magError                                                         NaN
magNst                                                           NaN
status                                                     automatic
locationSource                                                iscgem
magSource                                                   official
year                                                            1960
Name: 9869, dtype: object

7. Correlation between depth and magnitude¶

In [ ]:
df.plot(kind = 'scatter', x = 'depth', y = 'mag', title = 'Depth vs Magnitude')
Out[ ]:
<Axes: title={'center': 'Depth vs Magnitude'}, xlabel='depth', ylabel='mag'>

8. Percentage of Earthquakes by Magnitude Range¶

In [ ]:
bins = [5, 6, 7, 8, 9, 10]
labels = ['5-6', '6-7', '7-8', '8-9', '9-10']

df['mag_range'] = pd.cut(df['mag'], bins = bins, labels = labels, right = False)
magnitude_counts = df['mag_range'].value_counts(sort = False)
magnitude_percentages = magnitude_counts / magnitude_counts.sum() * 100

plt.figure(figsize = (10, 6))
magnitude_percentages.plot(kind = 'bar', color = '#ccff66')
plt.title('Percentage of Earthquakes by Magnitude Range (5-9)')
plt.xlabel('Magnitude Range')
plt.ylabel('Percentage of Earthquakes')
plt.xticks(rotation = 0)
plt.show()
In [ ]:
magnitude_percentages
Out[ ]:
5-6     86.024034
6-7     12.481923
7-8      1.395204
8-9      0.093638
9-10     0.005202
Name: mag_range, dtype: float64

9. Which are the top 10 countries with the highest number of earthquakes?¶

In [ ]:
import re

df['country'] = df['place'].apply(lambda x: re.split(', | of ', str(x))[-1])
top_countries = df['country'].value_counts().head(10)

neon_colors = [
    '#FF073A',
    '#39FF14',
    '#DFFF00',
    '#FE019A',
    '#B39EB5',
    '#FAED27',
    '#80DAEB',
    '#FFAA1D',
    '#FAA0FE',
    '#1B03A3'
]

plt.figure(figsize = (12, 6))
top_countries.plot(kind = 'bar', color = neon_colors)
plt.title('Top 10 Places with Most Earthquakes (1900-2023)')
plt.xlabel('Country')
plt.ylabel('Number of Earthquakes')
plt.xticks(rotation = 45)
plt.show()
In [ ]:
top_countries
Out[ ]:
Indonesia           11256
Japan                5952
Papua New Guinea     5917
Philippines          4414
Russia               4183
Tonga                4065
Alaska               3639
Chile                3148
Vanuatu              3147
Solomon Islands      2403
Name: country, dtype: int64

10. Top 20 Most Powerful Earthquakes¶

In [ ]:
top_20_earthquakes = df.sort_values(by = 'mag', ascending = False).head(20)
top_20_list = top_20_earthquakes[['year', 'place', 'mag']].reset_index(drop = True)


top_20_list
Out[ ]:
year place mag
0 1960 1960 Great Chilean Earthquake (Valdivia Earthq... 9.50
1 1964 1964 Prince William Sound Earthquake, Alaska 9.20
2 2004 2004 Sumatra - Andaman Islands Earthquake 9.10
3 2011 2011 Great Tohoku Earthquake, Japan 9.10
4 1952 89 km ESE of Petropavlovsk-Kamchatsky, Russia 9.00
5 2010 36 km WNW of Quirihue, Chile 8.80
6 1965 Rat Islands, Aleutian Islands, Alaska 8.70
7 1950 1950 Assam-Tibet Earthquake 8.60
8 2012 off the west coast of northern Sumatra 8.60
9 1957 81 km ESE of Adak, Alaska 8.60
10 2005 78 km WSW of Singkil, Indonesia 8.60
11 1946 1946 Aleutian Islands (Unimak Island) Earthqua... 8.60
12 1938 141 km WNW of Tual, Indonesia 8.50
13 1922 94 km ENE of Vallenar, Chile 8.50
14 1963 132 km ESE of Kuril’sk, Russia 8.50
15 1923 121 km ESE of Mil’kovo, Russia 8.40
16 1933 1933 Sanriku (Sanriku-oki) Earthquake, Japan 8.40
17 2001 6 km SSW of Atico, Peru 8.40
18 2007 122 km SW of Bengkulu, Indonesia 8.40
19 1905 121 km WNW of Tosontsengel, Mongolia 8.33

Cool Visualizations¶

1. 100 Years of Earthquakes¶

With Basemap¶

In [ ]:
!pip install Basemap
In [ ]:
import numpy as np
import matplotlib.patches as mpatches
from mpl_toolkits.basemap import Basemap

def get_marker_color_and_size(magnitude):
    if 5.0 <= magnitude < 6.0:
        return 'green', 10
    elif 6.0 <= magnitude < 7.0:
        return 'yellow', 60
    elif 7.0 <= magnitude < 8.0:
        return 'red', 200
    elif 8.0 <= magnitude < 9.0:
        return 'purple', 450
    elif 9.0 <= magnitude <= 9.5:
        return 'black', 700
    else:
        return 'gray', 0

plt.style.use('ggplot')
pd.set_option('display.max_columns', None)

fig = plt.figure(figsize = (15, 8))

eq_map = Basemap(projection = 'robin', resolution = 'l', area_thresh = 1000.0, lat_0 = 0, lon_0 = 0)

eq_map.drawcoastlines()
eq_map.drawcountries()
eq_map.fillcontinents(color = 'gray')
eq_map.bluemarble()
eq_map.drawmapboundary()
eq_map.drawmeridians(np.arange(0, 360, 30))
eq_map.drawparallels(np.arange(-90, 90, 30))

x, y = eq_map(df["longitude"].values, df["latitude"].values)

marker_colors_and_sizes = [get_marker_color_and_size(mag) for mag in df["mag"].values]
marker_colors, marker_sizes = zip(*marker_colors_and_sizes)

scatter = eq_map.scatter(x, y, s = marker_sizes, c = marker_colors, alpha = 0.7)

legend_handles = [
    mpatches.Patch(color = 'green', label = '5.0 - 6.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'yellow', label = '6.0 - 7.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'red', label = '7.0 - 8.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'purple', label = '8.0 - 9.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'black', label = '9.0 - 9.5 Magnitude', alpha = 1.0),
]

legend = plt.legend(handles = legend_handles, loc = 'upper left', fontsize = 12, scatterpoints = 1, labelspacing = 1,
           title = 'Magnitude Ranges', title_fontsize = '14', bbox_to_anchor = (1.05, 1), framealpha = 0.5)
for text in legend.get_texts():
    text.set_color('black')

legend.get_title().set_color('black')

plt.title("1900-10-09 through 2023-04-02", fontsize = 12, color = 'black')
subtitle = "100 Years of Earthquakes (1900-2023)"
plt.text(0.5, 0.9, subtitle, fontsize = 15, color = 'black', ha = 'center', transform = fig.transFigure)

plt.show()

WARNING:matplotlib.image:Clipping input data to the valid range for imshow with RGB data ([0..1] for floats or [0..255] for integers).

2. The Most Powerful Earthquakes: 1900-2023 (Magnitude 7.0+ Richter Scale)¶

Using Folium¶

In [ ]:
import folium
import branca

earthquakes_above_7 = df[df['mag'] > 7]

mean_lat = earthquakes_above_7['latitude'].mean()
mean_lon = earthquakes_above_7['longitude'].mean()

cust_tiles = 'https://server.arcgisonline.com/ArcGIS/rest/services/NatGeo_World_Map/MapServer/tile/{z}/{y}/{x}'
cust_attr = 'Tiles &copy; Esri &mdash; National Geographic, Esri, DeLorme, NAVTEQ, UNEP-WCMC, USGS, NASA, ESA, METI, NRCAN, GEBCO, NOAA, iPC'

earthquake_map = folium.Map(
    location = [mean_lat, mean_lon],
    tiles = cust_tiles,
    attr = cust_attr,
    zoom_start = 2
)

def get_color(magnitude):
    if 7 <= magnitude < 8:
        return 'red'
    elif 8 <= magnitude < 9:
        return 'purple'
    elif 9 <= magnitude <= 9.5:
        return 'black'
    else:
        return 'grey'

for _, earthquake in earthquakes_above_7.iterrows():
    location = [earthquake['latitude'], earthquake['longitude']]
    date_str = pd.to_datetime(earthquake['time']).strftime('%Y-%m-%d')
    time_str = pd.to_datetime(earthquake['time']).strftime('%H:%M:%S')
    tooltip = f"Date: {date_str}, Magnitude: {earthquake['mag']}, Location: {earthquake['place']}, Time: {time_str}"
    circle_color = get_color(earthquake['mag'])

    folium.CircleMarker(
        location,
        radius = earthquake['mag'] ** 5 / 2500,
        color = circle_color,
        opacity = 0.55,
        popup = f"""
LAT: {earthquake['latitude']},
LON: {earthquake['longitude']},
TIME: {earthquake['time']},
DEPTH: {earthquake['depth']},
MAG: {earthquake['mag']},
MAGTYPE: {earthquake['magType']},
NST: {earthquake['nst']},
GAP: {earthquake['gap']},
DMIN: {earthquake['dmin']},
RMS: {earthquake['rms']},
NET: {earthquake['net']},
ID: {earthquake['id']},
UPDATED: {earthquake['updated']},
PLACE: {earthquake['place']},
TYPE: {earthquake['type']},
HORIZONTAL ERROR: {earthquake['horizontalError']},
DEPTH ERROR: {earthquake['depthError']},
MAG ERROR: {earthquake['magError']},
MAG NST: {earthquake['magNst']},
STATUS: {earthquake['status']},
LOCATION SOURCE: {earthquake['locationSource']},
MAG SOURCE: {earthquake['magSource']},
YEAR: {earthquake['year']},
MAG RANGE: {earthquake['mag_range']},
COUNTRY: {earthquake['country']},
"""
,
        tooltip = tooltip
    ).add_to(earthquake_map)

url = 'https://raw.githubusercontent.com/fraxen/tectonicplates/master/GeoJSON/PB2002_boundaries.json'

folium.GeoJson(
    url,
    name = 'geojson'
).add_to(earthquake_map)

folium.LayerControl().add_to(earthquake_map)

year_select = earthquakes_above_7['year'].mode()[0]
title_html = '''
             <h3 align="center" style="font-size:16px"><b>The Most Powerful Earthquakes: 1900-2023 (Magnitude 7.0+ Richter Scale)</b></h3>
             '''.format(year_select)
earthquake_map.get_root().html.add_child(folium.Element(title_html))

legend_html = '''
<div style="position: fixed;
     bottom: 50px; left: 50px; width: 150px; height: 90px;
     border:2px solid grey; z-index:9999; font-size:14px;
     ">&nbsp; Magnitude RS<br>
     &nbsp; <i style="background:red; width:10px; height:10px; display:inline-block; opacity:0.7;"></i> 7.0 - 8.0 &nbsp; <br>
     &nbsp; <i style="background:purple; width:10px; height:10px; display:inline-block; opacity:0.7;"></i> 8.0 - 9.0 &nbsp; <br>
     &nbsp; <i style="background:black; width:10px; height:10px; display:inline-block; opacity:0.7;"></i> 9.0 - 9.5 &nbsp;
</div>
'''

earthquake_map.get_root().html.add_child(folium.Element(legend_html))

earthquake_map
Out[ ]:
Make this Notebook Trusted to load map: File -> Trust Notebook

3. Making an animation¶

import numpy as np
import pandas as pd
import matplotlib.pyplot as plt
import matplotlib.patches as mpatches
from mpl_toolkits.basemap import Basemap
import matplotlib.animation as animation
from matplotlib.animation import FuncAnimation


df['time'] = pd.to_datetime(df['time'])
df['year'] = df['time'].dt.year

def get_marker_color_and_size(magnitude):
    if 5.0 <= magnitude < 6.0:
        return 'green', 10
    elif 6.0 <= magnitude < 7.0:
        return 'yellow', 60
    elif 7.0 <= magnitude < 8.0:
        return 'red', 200
    elif 8.0 <= magnitude < 9.0:
        return 'purple', 450
    elif 9.0 <= magnitude <= 9.5:
        return 'black', 700
    else:
        return 'gray', 0

plt.style.use('ggplot')
pd.set_option('display.max_columns', None)

def update(year):
    plt.clf()
    fig = plt.figure(figsize = (15, 8))

    eq_map = Basemap(projection = 'robin', resolution = 'l', area_thresh = 1000.0, lat_0 = 0, lon_0 = 0)
    eq_map.drawcoastlines()
    eq_map.drawcountries()
    eq_map.fillcontinents(color = 'gray')
    eq_map.bluemarble()
    eq_map.drawmapboundary()
    eq_map.drawmeridians(np.arange(0, 360, 30))
    eq_map.drawparallels(np.arange(-90, 90, 30))

    year_data = df[df['year'] == year]

    x, y = eq_map(year_data["longitude"].values, year_data["latitude"].values)

    marker_colors_and_sizes = [get_marker_color_and_size(mag) for mag in year_data["mag"].values]
    marker_colors, marker_sizes = zip(*marker_colors_and_sizes)

    eq_map.scatter(x, y, s = marker_sizes, c = marker_colors, alpha = 0.7)

    legend_handles = [
    mpatches.Patch(color = 'green', label = '5.0 - 6.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'yellow', label = '6.0 - 7.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'red', label = '7.0 - 8.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'purple', label = '8.0 - 9.0 Magnitude', alpha = 0.7),
    mpatches.Patch(color = 'black', label = '9.0 - 9.5 Magnitude', alpha = 1.0),
    ]

    legend = plt.legend(handles = legend_handles, loc = 'upper left', fontsize = 12, scatterpoints = 1, labelspacing = 1,
           title = 'Magnitude Ranges', title_fontsize = '14', bbox_to_anchor = (1.05, 1), framealpha = 0.5)
    for text in legend.get_texts():
        text.set_color('black')

    legend.get_title().set_color('black')
    plt.title(f"Earthquakes in {year}", fontsize = 12, color = 'black')

start_year = df['year'].min()
end_year = df['year'].max()

for year in range(start_year, end_year + 1):
    update(year)
    plt.savefig(f'earthquake_{year}.png', bbox_inches = 'tight')
    plt.close()
In [ ]:
%%html
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Thank you for reading!¶

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References:¶

  1. TutorialsPoint. (n.d.). Analyze and Visualize Earthquake Data in Python with Matplotlib. Retrieved from https://www.tutorialspoint.com/analyze-and-visualize-earthquake-data-in-python-with-matplotlib

  2. MLearning AI. (n.d.). Earthquakes Data Visualization. Retrieved from https://medium.com/mlearning-ai/earthquakes-data-visualization-5da0d2abeea1

  3. CodeSpeedy. (n.d.). Analyze and Visualize Earthquake Data in Python with Matplotlib. Retrieved from https://www.codespeedy.com/analyze-and-visualize-earthquake-data-in-python-with-matplotlib/

  4. Intro to Python. (n.d.). Visualization of Earthquakes. Retrieved from http://introtopython.org/visualization_earthquakes.html

  5. Blanchard, J. (n.d.). Earthquake Visualization with Folium. Kaggle. Retrieved from https://www.kaggle.com/code/jonblanchard/earthquake-visualization-with-folium

  6. GitConnected. (n.d.). Plotting USGS Earthquake Data with Folium. Retrieved from https://levelup.gitconnected.com/plotting-usgs-earthquake-data-with-folium-8f11ddc21950